Senior RF Machine Learning Engineer

About Us:

At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.

Job Description:

Quartermaster AI is seeking a Senior AI/ML Engineer with an emphasis in RF analysis to develop and deploy machine learning systems that utilize RF data for real-time maritime intelligence.
You’ll work in a small team of experienced engineers to build detection, classification, and tagging models that help provide contextual understanding of vessel activity based on observed RF signatures.

Key Responsibilities:

• Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking.

• Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments.

• Build the interface between DSP feature outputs and model inputs by defining pre-processing, normalization, and feature extraction requirements in coordination with the DSP engineer.

• Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them.

• Optimize models and inference workflows for deployment on edge compute hardware.

• Document model architecture, training methodology, dataset provenance, and validation results.

Qualifications (Preferred):

• Master's or PhD in Machine Learning, Signal Processing, or a closely related field — or equivalent demonstrated experience.

• 5+ years building and deploying ML systems with a focus on RF or signals data.

• Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus.

• Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data.

• Proven ability to ship production models, not just research prototypes.

• Experience in maritime, aerospace, or operationally demanding spectral environments.

• Experience building labeled RF datasets from ground truth sources.

• Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation).

• Active Secret clearance or demonstrated ability to obtain one.